171–180 of 407 posts
I'm one of the authors of the bot, AMA
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So will this be the end of online poker?
It must be. It is way too hard to prevent humans from using an AI. Some chess services try to check if you're playing "too perfect", but in poker that's harder to do, and there's way more money on the line.
That's interesting, could you share an example? Most of my search results are anecdotal Reddit threads about how many people cheat in online chess.
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The same logic can apply to flop donk bets. Some flops favor the donking player's range more than their opponent.
Yea I'm not saying it's impossible to devise an unexploitable flop donking strategy. I think the reason thinking players generally don't is because of the complexity of adding significantly more branches early in the game tree - basically going from 3 (check-{fold,call,raise}) to 6 (those 3 plus donk-{fold,call,raise}).
I'm one of the authors of the bot, AMA
I haven't finished digging through the paper and the supplement yet, but I'm curious about how many hands were multiway to the flop (and whether the percentages differ significantly between 1H/5AI and 5H/1AI). I'd guess that it's a pretty small fraction of the total hands, and I'm wondering what the performance is like in those particular cases.
I'm one of the authors of the bot, AMA
Can you share more details about the abstraction? The paper is kind of vague on it. How does it decide if it should use 1 or 14 bet values? Is it a perfect recall abstraction? How many information sets are there?
I'm one of the authors of the bot, AMA
In your Science paper, you mention playing 1H-5AI against 2 human players: Chris Ferguson and Darren Elias. In your blog post you also mention playing 1H-5AI against Linus Loelinger, who was within standard error of even money. Why did Linus not make it into the Science paper?
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I haven't finished digging through the paper and the supplement yet, but I'm curious about how many hands were multiway to the flop (and whether the percentages differ significantly between 1H/5AI and 5H/1AI). I'd guess that it's a pretty small fraction of the total hands, and I'm wondering what the performance is like in those particular cases.
I don't have the exact percentages but I think it's less than 10%. It's not really possible to measure the bot's performance just in specific situations, but my feeling is the bot performs relatively well in these situations. Multi-way flops were basically impossible to do in a reasonable amount of time for past AI's. Our new search techniques make these situations feasible to figure out in seconds.
I'm one of the authors of the bot, AMA
The bot does not seem to consider previous hands in its decisions. That is to say, it does not consider who it is playing against. Should this affect how we perceive the bot as “strategic” or not? Bots that play purely mathematically optimally on expected value aren’t effective or interesting. But it feels like this is playing on just a much higher order expected value. It feels like a more down to earth version of t…
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I don't have the exact percentages but I think it's less than 10%. It's not really possible to measure the bot's performance just in specific situations, but my feeling is the bot performs relatively well in these situations. Multi-way flops were basically impossible to do in a reasonable amount of time for past AI's. Our new search techniques make these situations feasible to figure out in seconds.
Cheers, thanks. One of the reasons I asked about 1H/5AI vs 5H/1AI is that historically the new bots for a given form of poker have played a bit wider than the accepted wisdom of the time, so I was curious if there were relatively more multiway pots with 5AI than with 5H.
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I think it would be hard for the pros to find exploits against the bot, but they could definitely lose less. When using solvers, pros generally only input a couple of sizings for bets, and avoid 2x+ pot sizings, which from the video it seemed like the bot used at much higher frequencies than other pros.
I'm not great at poker, but I did play a decent amount and I know a lot of my strategy involves probing for other people's weaknesses and shifting my strategy mid game to throw people off. I feel like a lot of trained ML models have a lot laughable weaknesses, but perhaps they've been trained on every game they're well prepared for any tomfoolery.